Search Results (15,867)

Parkinson’s disease (PD) causes attentional deficits and worse dual-task (DT) performance, which increases the risk of being run over. In addition to motor deficits, the decision-making ability and the response to external stimuli are impaired. The aim of this study was to evaluate the spatiotemporal parameters of gait during everyday tasks of individuals with PD, specifically during street crossing simulation, obstacle circumvention, and motor DT. People with PD (PG) and matched controls (CG) were distributed into two groups and were evaluated under six different gait and randomized conditions: without a concomitant task (NW); with obstacle circumvention (OC); and four other conditions under simulation of street crossing (without concomitant task (SC); with obstacle circumvention (SC_OC); carrying bags (SC_B); and carrying bags concomitant to obstacle circumvention (SC_OC+B)). The CG group had greater values for all parameters compared to PG, except for double support time. This study’s results found that individuals with PD took smaller, narrower, slower, and shorter steps when compared to neurologically healthy older people and that there was a change in the spatiotemporal gait parameters of all individuals, except for the step-duration parameter under the most difficult crossing conditions. Full article

Objective: The research on turbid current deposition in shallow Marine shelf environments is relatively weak. Method: Based on three-dimensional seismic, drilling and logging data, etc., the spatio-temporal characterization of the shallow sea turbidity current sedimentary system was carried out by using seismic geomorphology and sedimentary numerical simulation techniques. Results and Conclusions: (1) A set of standards for identifying sedimentary units in the X Gas Field was established, identifying four sedimentary units: channel, mound body, channel-side accumulation body, and shelf mud; (2) The vertical evolution and planar distribution of the sedimentary units in the painting were precisely engraved. Along with the weakly–strongly–weak succession of turbidity current energy, the lithological combination of argillaceous siltstone–siltstone–mudstone developed vertically. On the plane, the clusters showed an evolution of isolation–connection–superposition. The scale of the river channel continued to expand, and the phenomena of oscillation and lateral accumulation occurred. (3) Three factors were analyzed: sea level, material sources, and sedimentary substrates (paleo landforms), and a shallow Marine turbidity current sedimentary system was established in the Honghe area in the northwest direction under the background of Marine receding, which is controlled by sedimentary slope folds and blocked by the high part of the diapause during the downward accumulation process of material sources along the shelf. (4) The numerical simulation results reconstructed the process of lateral migration of waterways, evolution of branch waterways into clusters, expansion of the scale of isolated clusters, and connection and superposition to form cluster complexes on a three-dimensional scale. The simulation results are in high agreement with the actual geological data. Full article

Wearable sensors are used in gait analysis to obtain spatiotemporal parameters, with gait events serving as critical markers for foot and lower limb movement. Summarizing detection methods is essential, as accurately identifying gait events and phases are key to deriving precise spatiotemporal parameters through wearable technology. However, a clear understanding of how these sensors, particularly angular velocity and acceleration signals within inertial measurement units, individually or collectively, contribute to the detection of gait events and gait phases is lacking. This review aims to summarize the current state of knowledge on the application for both gyroscopes, with particular emphasis on the role of angular velocity signals, and inertial measurement units with both angular velocity and acceleration signals in identifying gait events, gait phases, and calculating gait spatiotemporal parameters. Gyroscopes remain the primary tool for gait events detection, while inertia measurement units enhance reliability and enable spatiotemporal parameter estimation. Rule-based methods are suitable for controlled environments, whereas machine learning offers flexibility to analyze complex gait conditions. In addition, there is a lack of consensus on optimal sensor configurations for clinical applications. Future research should focus on standardizing sensor configurations and developing robust, adaptable detection methodologies suitable for different gait conditions. Full article

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects. Full article

To facilitate power system decarbonization, optimizing clean energy integration has emerged as a critical pathway for establishing sustainable power infrastructure. This study addresses the multi-timescale operational challenges inherent in power networks with high renewable penetration, proposing a novel stochastic dynamic programming framework that synergizes intraday microgrid dispatch with a multi-phase carbon cost calculation mechanism. A probabilistic carbon flux quantification model is developed, incorporating source–load carbon flow tracing and nonconvex carbon pricing dynamics to enhance environmental–economic co-optimization constraints. The spatiotemporally coupled multi-microgrid (MMG) coordination paradigm is reformulated as a continuous state-action Markov game process governed by stochastic differential Stackelberg game principles. A communication mechanism-enabled multi-agent twin-delayed deep deterministic policy gradient (CMMA-TD3) algorithm is implemented to achieve Pareto-optimal solutions through cyber–physical collaboration. Results of the measurements in the MMG containing three microgrids show that the proposed approach reduces operation costs by 61.59% and carbon emissions by 27.95% compared to the least effective benchmark solution. Full article

Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture designed to predict daily soil moisture (DSM) and daily actual evapotranspiration (DAE) by integrating climate data and geophysical insights, with a focus on mountainous areas such as the Adige catchment. The proposed framework aims to enhance the parameter-calibration quality. The process begins by mapping the statistical characteristics of DAE and DSM across the whole region using an unsupervised fusion technique. Model accuracy is assessed by comparing the similarity of Fuzzy C-Means (FCM) clusters before and after fusion, providing a metric for feature reduction. A data transformation technique using Gradient Boosting Regression (GBR) is then applied to each homogeneous subregion identified by the Random Forest classifier (RFC), based on elevation parameters (Wflow_dem). Furthermore, Kernel density estimation is used to ensure the reproducibility of the RFC-GBR process across large-scale applications. A comparative analysis is conducted across multiple SDL architectures, including LSTM, GRU, TCN, and ConvLSTM, over 50 epochs to better evaluate the beneficial effect of the transformed parameters on model performance and accuracy. Results indicate that adjusted parameter calibration improves model performance in all cases, with better alignment to Wflow ground truth during both wet and dry periods. The proposed model increases the accuracy by 20% to 42% when using simpler SDL models like LSTM and GRU, even with fewer epochs. Full article

(1) Understanding and characterizing the spatial and temporal variability of agricultural data is a key aspect of precision agriculture, particularly in soil management. Modeling the spatiotemporal dependency structure through geostatistical methods is essential for accurately estimating the parameters that define this structure and for performing Kriging-based interpolation. This study aimed to analyze the spatiotemporal variability of the soybean yield over ten crop years (2012–2013 to 2021–2022) in an agricultural area located in Cascavel, Paraná, Brazil. (2) Spatial analyses were conducted using two approaches: the Gaussian linear spatial model with independent multiple repetitions and the spatiotemporal model with a separable covariance structure. (3) The results showed that the maps generated using the Gaussian linear spatial model with multiple independent repetitions exhibited similar patterns to the individual soybean yield maps for each crop year. However, when comparing the kriged soybean yield maps based on independent multiple repetitions with those derived from the spatiotemporal model with a separable covariance structure, the accuracy indices indicated that the maps were dissimilar. (4) This suggests that incorporating the spatiotemporal structure provides additional information, making it a more comprehensive approach for analyzing soybean yield variability. The best model was chosen through cross-validation and a trace. Thus, incorporating a spatiotemporal model with a separable covariance structure increases the accuracy and interpretability of soybean yield analyses, making it a more effective tool for decision-making in precision agriculture. Full article

Fire-driven land cover change has generated a paradox: while habitat fragmentation from agriculture, livestock, and urban expansion has reduced natural fire occurrences, human-induced ignitions have increased wildfire frequency and intensity. In northern Colombia’s Magdalena Department, most of the territory faces moderate to high wildfire risk, especially during recurrent dry seasons and periods of below-average precipitation. However, knowledge of wildfire spatiotemporal occurrence and its drivers remains scarce. This work addresses this gap by identifying fire-prone zones and analyzing the influence of climate and vegetation in the Magdalena Department. Fire-prone zones were identified using the Getis–Ord Gi* method over fire density and burned area data from 2001 to 2023; then, they were analyzed with seasonally aggregated hydroclimatic indices via logistic regression to quantify their influence on wildfires. Vegetation susceptibility was assessed using geostatistics, obtaining land cover types most affected by fire and their degree of fragmentation. Fire-prone zones in the Magdalena Department covered ~744.35 km² (3.21%), with a weak but significant (τ = 0.20, p < 0.01) degree of coincidence between classification based on fire density, as pre-fire variable, and burned area, as a post-fire variable. Temporally, fire probability increased during the dry season, driven by short-lagged precursors such as Dry Spell Length and precipitation from the preceding wet season. Fire-prone zones were dominated by pastures (62.39%), grasslands and shrublands (19.61%) and forests (15.74%), and exhibited larger, more complex high-risk patches, despite similar spatial connectedness with non-fire-prone zones. These findings enhance wildfire vulnerability understanding, contributing to risk-based territorial planning. Full article

This study developed electrosprayed deferoxamine (DFO)-loaded poly(lactic-co-glycolic acid) microspheres (DFO-MS) combined with a sucrose acetate isobutyrate (SAIB) depot (DFO-MS@SAIB) for bone-defect repair, targeting the coordinated regulation of angiogenesis and osteogenesis in vascularized bone regeneration—where new blood vessels support functional bone integration. In vitro/in vivo evaluations confirmed its dual pro-angiogenic and pro-osteogenic effects via HIF-1α pathway activation. Background/Objectives: Emerging evidence underscores the indispensability of vascularization in bone-defect repair, a clinical challenge exacerbated by limited intrinsic healing capacity. While autologous grafts and growth-factor-based strategies remain mainstream, their utility is constrained by donor-site morbidity, transient bioactivity, and poor spatiotemporal control over angiogenic–osteogenic coupling. Here, we leveraged DFO, a hypoxia-mimetic HIF-1α stabilizer with angiogenic potential, to engineer an injectable DFO-MS@SAIB depot. This system was designed to achieve sustained DFO release, thereby synchronizing vascular network formation with mineralized tissue regeneration in critical-sized defects. Methods: DFO-MS were fabricated via electrospraying and combined with SAIB (DFO-MS@S) to form an injectable sustained-release depot. Their physicochemical properties, including morphology, encapsulation efficiency, degradation, release kinetics, and rheology, were systematically characterized. In vitro, the angiogenic capacity of HUVECs co-cultured with DFO-MS was evaluated; conditioned HUVECs were then co-cultured with BMSCs to assess the BMSCs’ cytocompatibility and osteogenic differentiation. In vivo bone regeneration in a rat calvarial defect model was evaluated using micro-CT, histology, and immunohistochemistry. Results: The DFO-MS@SAIB system achieved sustained DFO release, stimulating HUVEC proliferation, migration, and tubulogenesis. In a Transwell co-culture model, pretreated HUVECs promoted BMSC migration and osteogenic differentiation via paracrine signaling involving endothelial-secreted factors (e.g., VEGF). HIF-1α pathway activation upregulated osteogenic markers (ALP, Col1a1, OCN), while in vivo experiments demonstrated enhanced vascularized bone regeneration, with significantly increased bone volume/total volume (BV/TV) and new bone area compared with controls. Conclusion: The DFO-MS@SAIB system promotes bone regeneration via sustained deferoxamine release and HIF-1α-mediated signaling. Its angiogenesis–osteogenesis coupling effect facilitates vascularized bone regeneration, thereby offering a translatable strategy for critical-sized bone-defect repair. Full article

Urban rail transit, a crucial component of urban public transportation, often experiences increased operational costs and carbon emissions due to low-load operations being conducted during off-peak passenger flow periods. This study aims to develop an optimization method for the daily scheduling of rail train operations with the goal of carbon emission reduction, while comprehensively considering the built environment and travel demand. Firstly, the influence of the urban built environment on residents’ travel demand is analyzed using an XGBoost model. Secondly, a time convolutional travel demand prediction model, Built Environment-Weighted Temporal Convolutional Network (BE-TCN), weighted by built environment factors, is constructed. Finally, an optimization method for rail train operation schedules based on the built environment and travel demand is proposed, with the objective of carbon emission reduction. A case study is conducted using the Hangzhou urban rail transit system as an example. The results indicate that the optimization method proposed in this study can achieve monthly carbon emission reductions of 1524.58 tons, 1181.94 tons, and 520.84 tons for Lines 1, 2, and 4 of the Hangzhou urban rail transit system, respectively. The research findings contribute to enhancing the economic efficiency and environmental sustainability of urban rail transit systems. Full article

Testicular orphan receptors TR2 and TR4 serve as central regulators of erythropoiesis, orchestrating the entire continuum of erythroid progenitor cell proliferation, differentiation, and maturation. As core components of the direct repeat erythroid determinant (DRED) complex, they activate erythroid-specific transcriptional programs to dynamically control the spatiotemporal expression of globin genes. These nuclear receptors not only engage in functional interactions with key erythroid transcription factors GATA1 and KLF1 to coregulate erythroid differentiation and maturation but also recruit epigenetic modifier complexes such as DNMT1 and LSD1 to modulate chromatin states dynamically. Research has established that dysfunctions in TR2/TR4 are implicated in β-thalassemia and sickle cell disease (SCD): β-thalassemia is associated with the defective silencing of γ-globin genes, while in SCD, TR2/TR4 antagonizes BCL11A to reactivate fetal hemoglobin (HbF) expression. This review systematically dissects the molecular regulatory networks of TR2/TR4 in erythroid cells, interprets their dual regulatory properties across different stages of erythroid differentiation, and explores the therapeutic potential of targeting TR2/TR4 for treating erythroid-related disorders such as β-thalassemia and SCD, thereby providing novel directions for hematological disorder therapy. Full article

Land Surface Temperature (LST), as a core variable in the coupling of land–atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction method of the TIR-LST products from China’s Fengyun polar-orbiting satellite under dynamic cloud interference remains under exploration. This study focuses on the Heihe River Basin in western China, and addresses the issue of cloud coverage in relation to the Fengyun-3C (FY-3C) satellite TIR-LST. An innovative spatiotemporal reconstruction framework based on multi-source data collaboration was developed. Using a hybrid ensemble learning framework of random forest and ridge regression, environmental parameters such as vegetation index (NDVI), land cover type (LC), digital elevation model (DEM), and terrain slope were integrated. A downscaling and multi-factor collaborative representation model for land surface temperature was constructed, thereby integrating the passive microwave LST and thermal infrared VIRR-LST from the FY-3C satellite. This produced a seamless LST dataset with 1 km resolution for the period of 2017–2019, with temporal continuity across space. The validation results show that the reconstructed data significantly improves accuracy compared to the original VIRR-LST and demonstrates notable spatiotemporal consistency with MODIS LST at the daily scale (annual R² ≥ 0.88, RMSE < 2.3 K). This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking. Full article

Tongzhou District is the urban sub-center of Beijing, and the importance of groundwater resources is increasingly prominent. Based on groundwater level data from 1980 to 2020 and water usage data from various sectors in Tongzhou District between 2011 and 2020, this paper utilizes continuous wavelet transform (CWT), geostatistical models, and grey relational analysis (GRA) to explore the spatiotemporal evolution patterns and influencing factors of groundwater levels in Tongzhou District. The study reveals that the groundwater level evolution in Tongzhou District exhibits two primary cycles, and it predicts that the groundwater level at Liyuan Station will decrease and eventually rebound. From 1980 to 2020, the overall trend of groundwater levels in Tongzhou District showed a decline. However, the groundwater levels in the central and southern regions exhibited an upward trend from 2000 to 2020. The groundwater level is mainly influenced by spatial structural factors, with minimal impact from external random factors. Domestic water consumption, water usage in the tertiary sector, and industrial water usage have the greatest impact on groundwater levels, attributed to the rapid growth of the population and regional economy. Agricultural water usage has the least grey relational grade, which is related to changes in agricultural development planning in the study area, as well as reductions in the area of crop planting and the actual utilization area of facility agriculture. Full article

Mountain forests in biodiversity hotspots show complex responses to climate and topographic gradients. However, the effect of synergistic controls of elevation and climate on Net Primary Productivity (NPP) dynamics remain insufficiently quantified in complex mountains. Southwest China’s mountains are Asia’s most biodiverse temperate region with pronounced vertical ecosystem stratification, representing a critical continental carbon sink. This study investigated the spatiotemporal dynamics and driving mechanisms of NPP in Southwest China’s typical mountain ecosystems over the past three decades using a high-resolution modeling framework integrated with relative importance analysis, a Geodetector, and an elevation-dependent model. The results showed that (1) NPP revealed a significant increasing trend, rising from 634 ± 325 to 748 ± 348 g C m⁻² yr⁻¹ (mean rate 4 g C m⁻² yr⁻¹) from 1990 to 2018. Spatially, the most rapid increases occurred in eastern regions. (2) Rising CO₂ and climate warming (dominate 17% regions) drove interannual NPP growth, with elevation thresholds dictating driver dominance. The CO₂ governed low elevation, while temperature controlled higher elevation (>4800 m). (3) The elevation-dependent model revealed a more complex and nonlinear relationship between NPP and elevation, identifying three distinct phases: the saturation phase (<500 m) with negligible decay of NPP; the transition phase (500–3500 m) with linear decline (NPP loss of 29 g C m⁻² yr⁻¹ per 100 m); and the collapse phase (>3500 m) with continuously attenuated NPP losses (NPP average loss of 10.5 g C m⁻² yr⁻¹ per 100 m) reflecting high-elevation vegetation adaptation to extreme conditions. (4) Land cover dominated NPP spatial heterogeneity and was amplified by interactions with elevation and temperature, highlighting a vegetation–climate–topography coupling mechanism that critically shapes productivity patterns. Biodiversity-rich widespread mixed forests underpinned the region’s high productivity. Mountain protection should focus on protecting existing evergreen forests from fragmentation, while forestation should prioritize the establishment of biodiversity-rich mixed forest. These findings established a comprehensive framework for spatiotemporal analysis of driving mechanisms and enhanced the understanding of NPP dynamics in complex mountain ecosystems, informing sustainable management priorities in mountain regions. Full article

Epilepsy is a complex neurological disorder characterized by abnormal neural synchronization and interactions between local foci and global brain networks during seizures. Understanding seizure mechanisms across multiple scales is essential for advancing our understanding of epileptic network dynamics and guiding personalized treatment strategies. However, neural recording technologies are limited by insufficient spatial resolution, signal fidelity, and the inability to simultaneously capture network- and cellular-level dynamics. To address these limitations, we developed a high-density, flexible deep-brain probe with excellent mechanical compliance and wideband recording capabilities, enabling high-fidelity recordings of high-frequency oscillations (HFOs, 80–500 Hz) and action potentials (APs). Using a pentylenetetrazol (PTZ)-induced epilepsy model, we identified distinct spatiotemporal dynamics of HFOs and APs across epileptic stages, indicating that CA3 plays a key role in seizure onset, while CA1 is crucial for propagation. AP-HFO coupling analysis further uncovered neuronal heterogeneity, offering insights into the diverse roles of neurons in epileptic networks. This study highlights the potential of a flexible deep-brain probe for advancing epilepsy research and guiding personalized therapeutic interventions. Full article